In many TDMA (Time Division Multiple Access) based radio receivers there is used a functional block known as the signal equalizer for recovering transmitted data from a received signal which has been corrupted by noise and intersymbol interference. The task of recovering the transmitted data is usually denoted as either equalizing or detecting for short. Typical radio receivers that use a signal equalizer are the receiver parts in mobile stations and base stations of cellular radio systems. A signal equalizer needs to know the impulse response of the radio channel to a certain extent for the equalization to be successful.
FIG. 1 illustrates a known arrangement where an analog oscillating signal on a radio frequency is received through an antenna 101, downconverted onto a baseband frequency in a radio receiver 102 and converted into a series of digital samples in an A/D converter 103. The samples are led both into a channel estimator block 104 and a signal equalizer block 105. The former uses a certain part of the received signal (usually known as the training sequence) to estimate the impulse response of the radio channel, and provides the impulse response estimate to the signal equalizer block 105. The signal equalizer block 105 performs the equalization and gives at its output both the recovered data (the so-called hard decision output) and some reliability information (soft decision output) associated with the recovered data. These are passed on to a channel decoder 106 which removes all channel decoding (if any) from the original information symbol sequence which was subjected to transmission. The channel decoding operation may comprise additional operations like de-interleaving, and the reconstructed information symbols may be conveyed further e.g. to an audio or video decoder, to a data storage device or to some control circuitry.
In a TDMA system the received signal consists of discrete transmission blocks, each of which is received in a certain time slot. The conventional way of performing channel estimation and signal equalization is to use a training sequence with known contents within the transmission block to generate an estimate of the radio channel's impulse response (also known as the channel estimate for short), to fix the equalization function on the basis of the obtained channel estimate and to equalize the whole received transmission block by using the same equalization function. The drawback of the traditional scheme is that it requires a relatively long training sequence for the channel estimation to succeed, so that for a remarkable percentage of time the radio channel is reserved for the transmission of fixed training symbol values instead of useful data. Even then the performance of the channel estimation and signal equalization operations may not always be best possible.
There has been proposed a so-called iterative equalization or turbo equalization approach which means that the channel decoder 106 provides the signal equalizer 105 with information concerning the results of the channel decoding process, and the signal equalizer performs a new equalization round on the basis of the provided information. The feedback connection that is needed for performing iterative equalization is shown as a dashed line in FIG. 1. Basically the iteration rounds, i.e. the consecutive times of equalizing, decoding and providing updated information from the decoder to the signal equalizer, may be repeated for an arbitrary number of times. However, that approach has not been found to remarkably improve the performance of a receiver in all cases.
An article “Iterative channel estimation using soft decision feedback” by M. Sandell, C. Luschi, P. Strauch and R. Yan, GLOBECOM'98, pp. 3728-3733, December 1998, presents an iterative channel estimation scheme with either hard or soft decision feedback from a the channel decoding stage to the channel estimator. In the proposed arrangement the channel estimator calculates a new channel estimate from a sequence of symbol decisions it receives from either the signal equalizer or the channel decoder or both. The authors claim that an iteratively refined channel estimate results in enhanced bit error ratios.